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metadata
title: Albert Latest 96
emoji: π¨
colorFrom: blue
colorTo: pink
sdk: docker
pinned: false
short_description: AI model to classify accessibility-related bugs.
Accessibility Bug Prediction Using ALBERT π
This project leverages the ALBERT (A Lite BERT) model to classify software bug reports into two categories:
- Accessibility-related bugs.
- Non-accessibility bugs.
It also includes a custom Jira plugin to integrate the AI model into the bug-tracking workflow, making it easier for development teams to identify and prioritize accessibility issues.
Key Features β¨
- State-of-the-Art NLP: Utilizes the ALBERT transformer model, fine-tuned for high accuracy on bug report classification tasks.
- Custom Dataset: The model was trained from scratch on a dataset collected by the research team.
- Jira Plugin Integration: Seamlessly integrates the classification system into Jira to enhance accessibility compliance workflows.
- Research Collaboration: Developed under the guidance of Professor Wajdi Aljedaani, a UX and Human-Centered AI researcher.
How It Works π
- Input: Provide a textual description of a bug report.
- Prediction: The ALBERT model analyzes the text and classifies the bug as either accessibility-related or not.
- Output: Use the results directly or integrate them into Jira for workflow optimization.
Applications π οΈ
- Software Development: Identify accessibility bugs to ensure compliance with standards like WCAG.
- Quality Assurance: Optimize testing and prioritization for accessibility-related issues.
- Research in UX and AI: Leverage insights for designing inclusive and accessible systems.
Deployment π
The model is hosted on Hugging Face Spaces, providing an interactive and user-friendly web interface.
About the Research π€
This project was developed as part of a research initiative at UNT under Professor Wajdi Aljedaani's guidance. It emphasizes the intersection of AI, UX, and accessibility to drive impactful solutions for software development.
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference